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production data

Think back just 5 years ago. In 2014…
• The seminal DevOps book—Gene Kim’s The Phoenix Project—was one year old
• Gartner predicted that 25% of Global 2000 enterprises would adopt DevOps to some extent by 20161
• "Continuous Testing” just started appearing in industry publications and conferences2
• Many of today’s popular test frameworks were brand new—or not yet released
• The term “microservices” was just entering our lexicon
• QC/UFT and ALM were still sold by HP (not even HPE yet)
• Only 30% of enterprise software testing was performed fully “in house”3
• There was no GDPR restricting the use of production data for software testing
• Packaged apps were typically updated on an annual or semi-annual basis and modern platforms like
SAP S/4HANA and Salesforce Lightning hadn’t even been announced
Times have changed—a lot. If the way that you’re testing hasn’t already transformed dramatically, it will soon.
And the pace and scope of disruption will continue to escalate throughout the fo

The Industrial Internet of Things (IIoT) is flooding today’s industrial sector with data. Information is streaming in from many sources — equipment on production lines, sensors at customer facilities, sales data, and much more. Harvesting insights means filtering out the noise to arrive at actionable intelligence.
This report shows how to craft a strategy to gain a competitive edge. It explains how to evaluate IIoT solutions, including what to look for in end-to-end analytics solutions. Finally, it shows how SAS has combined its analytics expertise with Intel’s leadership in IIoT information architecture to create solutions that turn raw data into valuable insights.

Dell EMC technology for Digital Manufacturing harnesses the workstation, HPC and storage capabilities that combine to enable better products, more efficient design and production processes, and meet rapidly changing customer preferences.
Collecting, collating and digesting more and more data in the entire ecosystem, from product modelling to after-sales trends, are making the digital factory a powerful and necessary reality in the manufacturing landscape.

Dell EMC technology for Digital Manufacturing harnesses the workstation, HPC and storage capabilities that combine to enable better products, more efficient design and production processes, and meet rapidly changing customer preferences.
Collecting, collating and digesting more and more data in the entire ecosystem, from product modelling to after-sales trends, are making the digital factory a powerful and necessary reality in the manufacturing landscape.
Learn more about Dell Precision® workstations featuring Intel® Xeon® processors

Dell EMC technology for Digital Manufacturing harnesses the workstation, HPC and storage capabilities that combine to enable better products, more efficient design and production processes, and meet rapidly changing customer preferences.
Collecting, collating and digesting more and more data in the entire ecosystem, from product modelling to after-sales trends, are making the digital factory a powerful and necessary reality in the manufacturing landscape.
Learn more about Dell Precision® workstations featuring Intel® Xeon® processors

High-profile data breaches continue to make headlines as organizations struggle to manage information security in the face of rapidly changing applications, data centers, and the cloud. Against this backdrop, data masking has emerged as one of the most effective ways to protect sensitive test data from insider and outsider threats alike.
While masking is now the de facto standard for protecting non-production data, implementing it alongside virtual data technologies has elevated its effectiveness even further.

Today's test data management (TDM) solutions force teams to work with compromised data sets, and push testing to too late in the software development lifecycle. The end result is rework, delayed releases, and costly bugs that cripple production systems. Furthermore, prevailing approaches to test data management - including subsetting, synthetic data, shared environments, and standalone masking--represent flawed solutions that fail across one or more key dimensions.

The current trend in manufacturing is towards tailor-made products in smaller lots with shorter delivery times. This change may lead to frequent production modifications resulting in increased machine downtime, higher production cost, product waste—and no need to rework faulty products. To satisfy the customer demand behind this trend, manufacturers must move quickly to new production models. Quality assurance is the key area that IT must support. At the same time, the traceability of products becomes central to compliance as well as quality. Traceability can be achieved by interconnecting data sources across the factory, analyzing historical and streaming data for insights, and taking immediate action to control the entire end-to-end process. Doing so can lead to noticeable cost reductions, and gains in efficiency, process reliability, and speed of new product delivery. Additionally, analytics helps manufacturers find the best setups for machinery.

Organizations around the world are benefiting from public clouds. But, when production applications or critical data is involved, it's important to extend on-premise governance to your public or hybrid cloud resources. Effective cloud governance is possible.

This paper introduces five architectural principles guiding the development of the next generation data center (NGDC). It describes key market influences leading a fundamental enterprise IT transformation and the technological trends that support it. The five principles are: scale-out, guaranteed performance, automated management, data assurance, and global efficiencies. Cloud infrastructure delivery models such as IaaS, private clouds, and software-defined data centers (SDDC) are foundations for the NGDC. In an era where IT is expected to ensure productiongrade support with a plethoric flow of new applications and data, these models demonstrate how to eliminate bottlenecks, increase self-service, and move the business forward. The NGDC applies a software-defined everything (SDx) discipline in a traditional, hardware-centric business to gain business advantage.

There are five ways to provision test data. You can copy or take a snapshot of your production database or databases. You can provision data manually or via a spreadsheet. You can derive virtual copies of your production database(s).
You can generate subsets of your production database(s). And you can generate synthetic data that is representative of your production data but is not actually real. Of course, the first four examples assume that the data you need for testing purposes is available to you from your production databases.
If this is not the case, then only manual or synthetic data provision is a viable option.
Download this whitepaper to find out more about how CA Technologies can help your business and its Test Data problems.

Traditional backup systems fail to meet the database protection and recovery requirements of modern organizations. These systems require ever-growing backup windows, negatively impact performance in mission-critical production databases, and deliver recovery time objectives (RTO) and recovery point objectives (RPO) measured in hours or even days, failing to meet the requirements of high-volume, high transactional databases -- potentially costing millions in lost productivity and revenue, regulatory penalties, and reputation damage due to an outage or data loss.

Challenge
It is not uncommon for SAP system copies, including any post-editing, to take several days to complete. Meanwhile, testing, development and training activities come to a standstill, and the large number of manual tasks in the entire process ties up highly skilled SAP BASIS staff.
Opportunity
Enterprises are looking to automation as a way to accelerate SAP system copies and free up staff. However, this is only one part of the problem: What further complicates the system copy process is the need to safeguard sensitive data and manage huge data volumes while also ensuring that the data used in non-production systems adequately reflects the data in production systems so the quality of development, testing and training activities is not compromised.
Benefits
This white paper explains how a considerable portion of the SAP system copy process can be automated using the CA Automic Automated System Copy for SAP solution and SNP T-Bone, helping enterprises become more agile.

In April 2014, Nissan opened a new production plant in Rio de Janeiro, Brazil with an annual production capacity of 200,000 vehicles and 200,000 engines.
Along with a target of reaching 5% market share in Brazil by 2016, the plant’s opening was aligned with Nissan’s efforts to achieve 8% global market share, part of the “NISSAN POWER 88” five-year mid-term management plan initiated in 2011.
To help align their strategic and business goals with their use of technology, Nissan chose Red Hat JBoss BRMS to replace their legacy system.

"The Implications for Test Data Management
The GDPR is set to have wide-ranging implications for the type of data which can be used in non-production environments. Organizations will need to understand exactly what data they have and who’s using it, and be able to restrict its use to tasks where they have consent.
Learn more about how you can protect the data that matters most and comply with the GDPR."

"There's new legislation in place, that's expanded the definition of personal data and puts IT and testing departments on high alert to safeguard personal data, across testing and development environments. It's the General Data Protection Regulation (GDPR). Are you ready for it?
In this session, we’ll demonstrate how CA Test Data Manager helps to both mask your production data and to generate synthetic test data; a powerful combination to help you meet compliance needs and deliver quality applications. There will be a short section on the future of the tester self-service model that will enable testers to efficiently get access to the right test data."

Change data capture (CDC) technology can modernize your data and analytics environment with scalable, efficient and real-time data replication that does not impact production systems.
To realize these benefits, enterprises need to understand how this critical technology works, why it’s needed, and what their Fortune 500 peers have learned from their CDC implementations. This book serves as a practical guide for enterprise architects, data managers and CIOs as they enable modern data lake, streaming and cloud architectures with CDC.
Read this book to understand:
? The rise of data lake, streaming and cloud platforms
? How CDC works and enables these architectures
? Case studies of leading-edge enterprises
? Planning and implementation approaches

Keeping the lights on in a manufacturing environment remains top priority for industrial companies. All too often, factories are in a reactive mode, relying on manual inspections that risk downtime because they don’t usually reveal actionable problem data.
Find out how the Nexcom Predictive Diagnostic Maintenance (PDM) system enables uninterrupted production during outages by monitoring each unit in the Diesel Uninterrupted Power Supplies (DUPS) system noninvasively.
• Using vibration analysis, the system can detect 85% of power supply problems before they do damage or cause failure
• Information processing for machine diagnostics is done at the edge, providing real-time alerts on potential issues with ample of lead time for managers to rectify
• Graphic user interface offers visual representation and analysis of historical and trending data that is easily consumable

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